Many businesses rely on legacy mainframe computer systems for critical day-to-day operation. For example, many loan servicers use so-called “transaction card” mainframes for processing transactions related to issuing new loans and modifying existing loans. Mainframe systems may require that data be imported using a specific, proprietary data formats defined by the mainframe manufacture. For example, a transaction card mainframe may require that each transaction card be specified using a fixed-length string of characters with a defined set of options for each position in the string. Such data formats can be very difficult for humans to understand (i.e., not “human readable”) and may require a highly customized programming to generate compliant data. On the other hand, modern end-user applications typically support flexible, open, and human-readable data formats such as JavaScript Object Notation (JSON) and Extensible Markup Language (XML), along with more modern application programming interfaces (APIs) such as Representational State Transfer (REST)-based APIs. Moreover, mainframe systems may be designed to import data in batches, for example using batch files that are processed only once per day. If the input data does not conform to the mainframe's rigidly-defined data format, this error may not be reported until the next business day. Further, existing transactional mainframes may not provide any capabilities for monitoring the status of submitted transactions, re-submitting failed transactions, or rolling back transactions.